Thermophysical properties are essential to all aspects of the chemical industry. While accurate experimental measurements are required for design purposes, measurements alone can't possibly satisfy current needs. The ultimate objective of this research is development of universal and consistent methods for prediction of these properties from a small data base of fundamental parameters. The attack on this problem used by Professor Rowley is three fold:
Molecular Modeling
A relatively new and exciting method is the use of molecular dynamics simulations to study the relationship between intermolecular interactions and measured thermophysical properties. In this method, Newton's equations of motion are solved for a collection of molecules and thermophysical properties are then calculated from the simulated molecular motion. Virtually any thermophysical property can be obtained from the simulations. The significance of this approach lies in the fundamental nature of the molecular interactions. Once accurate interaction models are developed for the fluid in question, the simulations can be used to predict any other thermophysical property at any condition.
Computational Chemistry
With today's computers and commercially available quantum mechanical software, ab initio calculations of complex molecules and systems can be made with good accuracy. Dr. Rowley's group performs energy calculations on clusters of molecules (often just a pair) to ascertain the potential energy of the cluster relative to the separated molecules. This can be done at various separation distances and relative orientations in order to obtain a potential energy surface. From this information, pair-wise additive interatomic potentials can be obtained. This allows us to construct a potential model for any molecule comprised of the same type of atoms as in the cluster that we examined. These model potentials can then be used in molecular simulations to obtain the forces between the molecules at each time step.
Thermophysical Property Database (DIPPR database)
The DIPPR database is uniquely suited for rapid development of property correlations because of its evaluated nature. Not only are the most accurate data recommended, but quality codes are given for the data. This permits one to select data that are of a consistent accuracy, say 2%, as a training set from which QSPR (Quantitative Structure-Property Relationship) correlations can be developed. Group contributions and other molecular structure contributions are being developed from the DIPPR database for a wide range of properties.
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